af96579c76
* Update installation guide for pipeline parallel inference * Small fix * further fix * Small fix * Small fix * Update based on comments * Small fix * Small fix * Small fix
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3.7 KiB
Markdown
94 lines
No EOL
3.7 KiB
Markdown
# Run IPEX-LLM on Multiple Intel GPUs in Pipeline Parallel Fashion
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This example demonstrates how to run IPEX-LLM optimized low-bit model vertically partitioned on multiple [Intel GPUs](../README.md) for Linux users.
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## Requirements
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To run this example with IPEX-LLM on Intel GPUs, we have some recommended requirements for your machine, please refer to [here](../README.md#recommended-requirements) for more information. For this particular example, you will need at least two GPUs on your machine.
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> [!NOTE]
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> To run IPEX-LLM on multiple Intel GPUs in pipeline parallel fashion, you will need to install **Intel® oneAPI Base Toolkit 2024.1**, which could be done through an offline installer:
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> ```bash
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> wget https://registrationcenter-download.intel.com/akdlm/IRC_NAS/fdc7a2bc-b7a8-47eb-8876-de6201297144/l_BaseKit_p_2024.1.0.596_offline.sh
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>
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> sudo sh ./l_BaseKit_p_2024.1.0.596_offline.sh
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> ```
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## Example: Run pipeline parallel inference on multiple GPUs
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### 1. Installation
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```bash
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conda create -n llm python=3.11
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conda activate llm
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pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
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pip install torch==2.1.0.post2 torchvision==0.16.0.post2 torchaudio==2.1.0.post2 intel-extension-for-pytorch==2.1.30+xpu oneccl_bind_pt==2.1.300+xpu --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
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```
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### 2. Configures OneAPI environment variables
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```bash
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source /opt/intel/oneapi/setvars.sh
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```
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> [!NOTE]
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> Please make sure you configure the environment variables for **Intel® oneAPI Base Toolkit's version == 2024.1.**.
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### 3 Runtime Configurations
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For optimal performance, it is recommended to set several environment variables. Please check out the suggestions based on your device.
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<details>
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<summary>For Intel Arc™ A-Series Graphics and Intel Data Center GPU Flex Series</summary>
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```bash
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export USE_XETLA=OFF
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export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
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export SYCL_CACHE_PERSISTENT=1
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```
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</details>
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<details>
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<summary>For Intel Data Center GPU Max Series</summary>
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```bash
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export LD_PRELOAD=${LD_PRELOAD}:${CONDA_PREFIX}/lib/libtcmalloc.so
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export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
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export SYCL_CACHE_PERSISTENT=1
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export ENABLE_SDP_FUSION=1
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```
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> [!NOTE]
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> Please note that `libtcmalloc.so` can be installed by `conda install -c conda-forge -y gperftools=2.10`.
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</details>
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### 4. Running examples
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```
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python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt PROMPT --n-predict N_PREDICT --gpu-num GPU_NUM
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```
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Arguments info:
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- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the Llama2 model (e.g. `meta-llama/Llama-2-7b-chat-hf` and `meta-llama/Llama-2-13b-chat-hf`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'meta-llama/Llama-2-7b-chat-hf'`.
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- `--prompt PROMPT`: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be `'What is AI?'`.
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- `--n-predict N_PREDICT`: argument defining the max number of tokens to predict. It is default to be `32`.
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- `--gpu-num GPU_NUM`: argument defining the number of GPU to use. It is default to be `2`.
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#### Sample Output
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##### [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)
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```log
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Inference time: xxxx s
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-------------------- Prompt --------------------
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<s>[INST] <<SYS>>
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<</SYS>>
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What is AI? [/INST]
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-------------------- Output --------------------
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[INST] <<SYS>>
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<</SYS>>
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What is AI? [/INST] Artificial intelligence (AI) is the broader field of research and development aimed at creating machines that can perform tasks that typically require human intelligence,
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``` |